In the cold wave weather, it is quite difficult to forecast the ice coating of wind turbines in mountain areas, and the prediction deviation of wind power is large. The accurate temperature prediction of wind turbines is the key to improve the prediction accuracy of the ice coating period. Due to the heterogeneity and volatility of air temperature, it is hard for a single model to adapt to the demand for air temperature prediction of different wind farms. This paper presents a multi-mode fusion fan temperature prediction method (MMTP), which not only improves the universality of prediction, but also realizes the complementary advantages of different models by using the combined weight method of AHP-independence-entropy method, and several artificial intelligence algorithms. The prediction results show that: in the 48 h forecast time, the MMTP significantly improves the temperature correction effect compared with the single mode, and the temperature forecast error is decreased by 8%. Besides, the fan icing period forecast is more accurate after temperature correction using this method within the 48 h forecast period, and the forecast error of the glacial period is reduced by 3-6 hours.